# Stable Diffusion v1.5

> Stable Diffusion v1.5


## Overview

- **Endpoint**: `https://fal.run/fal-ai/stable-diffusion-v15`
- **Model ID**: `fal-ai/stable-diffusion-v15`
- **Category**: text-to-image
- **Kind**: inference
**Tags**: diffusion



## Pricing

- **Price**: $0 per compute seconds

For more details, see [fal.ai pricing](https://fal.ai/pricing).

## API Information

This model can be used via our HTTP API or more conveniently via our client libraries.
See the input and output schema below, as well as the usage examples.


### Input Schema

The API accepts the following input parameters:


- **`prompt`** (`string`, _required_):
  The prompt to use for generating the image. Be as descriptive as possible for best results.
  - Examples: "photo of a rhino dressed suit and tie sitting at a table in a bar with a bar stools, award winning photography, Elke vogelsang", "Photo of a classic red mustang car parked in las vegas strip at night"

- **`negative_prompt`** (`string`, _optional_):
  The negative prompt to use. Use it to address details that you don't want
  in the image. This could be colors, objects, scenery and even the small details
  (e.g. moustache, blurry, low resolution). Default value: `""`
  - Default: `""`
  - Examples: "cartoon, illustration, animation. face. male, female", "ugly, deformed"

- **`image_size`** (`ImageSize | Enum`, _optional_):
  The size of the generated image. Default value: `square`
  - Default: `"square"`
  - One of: ImageSize | Enum

- **`num_inference_steps`** (`integer`, _optional_):
  The number of inference steps to perform. Default value: `25`
  - Default: `25`
  - Range: `1` to `50`

- **`seed`** (`integer`, _optional_):
  The same seed and the same prompt given to the same version of Stable Diffusion
  will output the same image every time.

- **`guidance_scale`** (`float`, _optional_):
  The CFG (Classifier Free Guidance) scale is a measure of how close you want
  the model to stick to your prompt when looking for a related image to show you. Default value: `7.5`
  - Default: `7.5`
  - Range: `0` to `20`

- **`sync_mode`** (`boolean`, _optional_):
  If `True`, the media will be returned as a data URI and the output data won't be available in the request history.
  - Default: `false`

- **`num_images`** (`integer`, _optional_):
  The number of images to generate. Default value: `1`
  - Default: `1`
  - Range: `1` to `8`

- **`loras`** (`list<LoraWeight>`, _optional_):
  The list of LoRA weights to use.
  - Default: `[]`
  - Array of LoraWeight

- **`embeddings`** (`list<Embedding>`, _optional_):
  The list of embeddings to use.
  - Default: `[]`
  - Array of Embedding

- **`enable_safety_checker`** (`boolean`, _optional_):
  If set to true, the safety checker will be enabled. Default value: `true`
  - Default: `true`

- **`safety_checker_version`** (`SafetyCheckerVersionEnum`, _optional_):
  The version of the safety checker to use. v1 is the default CompVis safety checker. v2 uses a custom ViT model. Default value: `"v1"`
  - Default: `"v1"`
  - Options: `"v1"`, `"v2"`

- **`expand_prompt`** (`boolean`, _optional_):
  If set to true, the prompt will be expanded with additional prompts.
  - Default: `false`

- **`format`** (`FormatEnum`, _optional_):
  The format of the generated image. Default value: `"jpeg"`
  - Default: `"jpeg"`
  - Options: `"jpeg"`, `"png"`

- **`request_id`** (`string`, _optional_):
  An id bound to a request, can be used with response to identify the request
  itself. Default value: `""`
  - Default: `""`



**Required Parameters Example**:

```json
{
  "prompt": "photo of a rhino dressed suit and tie sitting at a table in a bar with a bar stools, award winning photography, Elke vogelsang"
}
```

**Full Example**:

```json
{
  "prompt": "photo of a rhino dressed suit and tie sitting at a table in a bar with a bar stools, award winning photography, Elke vogelsang",
  "negative_prompt": "cartoon, illustration, animation. face. male, female",
  "image_size": "square",
  "num_inference_steps": 25,
  "guidance_scale": 7.5,
  "num_images": 1,
  "loras": [],
  "embeddings": [],
  "enable_safety_checker": true,
  "safety_checker_version": "v1",
  "format": "jpeg"
}
```


### Output Schema

The API returns the following output format:

- **`images`** (`list<Image>`, _required_):
  The generated image files info.
  - Array of Image

- **`timings`** (`Timings`, _required_)

- **`seed`** (`integer`, _required_):
  Seed of the generated Image. It will be the same value of the one passed in the
  input or the randomly generated that was used in case none was passed.

- **`has_nsfw_concepts`** (`list<boolean>`, _required_):
  Whether the generated images contain NSFW concepts.
  - Array of boolean

- **`prompt`** (`string`, _required_):
  The prompt used for generating the image.



**Example Response**:

```json
{
  "images": [
    {
      "url": "",
      "content_type": "image/jpeg"
    }
  ],
  "prompt": ""
}
```


## Usage Examples

### cURL

```bash
curl --request POST \
  --url https://fal.run/fal-ai/stable-diffusion-v15 \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
     "prompt": "photo of a rhino dressed suit and tie sitting at a table in a bar with a bar stools, award winning photography, Elke vogelsang"
   }'
```

### Python

Ensure you have the Python client installed:

```bash
pip install fal-client
```

Then use the API client to make requests:

```python
import fal_client

def on_queue_update(update):
    if isinstance(update, fal_client.InProgress):
        for log in update.logs:
           print(log["message"])

result = fal_client.subscribe(
    "fal-ai/stable-diffusion-v15",
    arguments={
        "prompt": "photo of a rhino dressed suit and tie sitting at a table in a bar with a bar stools, award winning photography, Elke vogelsang"
    },
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)
```

### JavaScript

Ensure you have the JavaScript client installed:

```bash
npm install --save @fal-ai/client
```

Then use the API client to make requests:

```javascript
import { fal } from "@fal-ai/client";

const result = await fal.subscribe("fal-ai/stable-diffusion-v15", {
  input: {
    prompt: "photo of a rhino dressed suit and tie sitting at a table in a bar with a bar stools, award winning photography, Elke vogelsang"
  },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});
console.log(result.data);
console.log(result.requestId);
```


## Additional Resources

### Documentation

- [Model Playground](https://fal.ai/models/fal-ai/stable-diffusion-v15)
- [API Documentation](https://fal.ai/models/fal-ai/stable-diffusion-v15/api)
- [OpenAPI Schema](https://fal.ai/api/openapi/queue/openapi.json?endpoint_id=fal-ai/stable-diffusion-v15)
- [GitHub Repository](https://huggingface.co/runwayml/stable-diffusion-v1-5)

### fal.ai Platform

- [Platform Documentation](https://docs.fal.ai)
- [Python Client](https://docs.fal.ai/clients/python)
- [JavaScript Client](https://docs.fal.ai/clients/javascript)